Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation

Marine Hamon, Vincent Lemaire, Nour Eddine Yassine Nair-Benrekia, Samuel Berlemont, Julien Cumin
1/14/2025
cs.LG

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Abstract

To detect anomalies with precision and without prior knowledge in time series, is it better to build a detector from the initial temporal representation, or to compute a new (tabular) representation using an existing automatic variable construction library? In this article, we address this question by conducting an in-depth experimental study for two popular detectors (Isolation Forest and Local Outlier Factor). The obtained results, for 5 different datasets, show that the new representation, computed using the tsfresh library, allows Isolation Forest to significantly improve its performance.

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Cite this paper

@article{hamon2025unsupervised,
  title  = {Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation},
  author = {Marine Hamon and Vincent Lemaire and Nour Eddine Yassine Nair-Benrekia and Samuel Berlemont and Julien Cumin},
  year   = {2025},
  eprint = {2501.07999},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.07999v2}
}

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